Hybrid Model and Split Bregman Iteration Algorithm for Image Denoising

摘要

Although difference of convex model has attracted many research efforts due to its superior performance for image processing, no attention has focused on robust data fidelity for this model. In this paper, we propose a novel model, which combines the l_1 and l_2 fidelity terms with a weighted difference of anisotropic and isotropic total variation (TV). Since our model takes a new difference form of convex terms, we employ difference of convex algorithm (DCA). In this paper, we adopt split Bregman iteration (SBI) to solve each DCA subproblem of the proposed model. Image denoising verifies the convergence of optimal solution and monotone decreasing of objective function. Experimental results on image denoising demonstrate that the proposed methods outperform other competing methods in terms of quantitative criteria and perceptual quality.